code: def parse_args():
      parser = argparse.ArgumentParser(
          "Train the unsupervised human pose estimation network"
      )
      parser.add_argument(
          "--cfg",
          help="Specify the path of the path of the config(*.yaml)",
          default="../cfg/default.yaml",
      )
      parser.add_argument(
          "--use_gt",
          action="store_true",
          help="Specify whether to use 2d gt / predictions as inputs",
      )
      parser.add_argument(
          "--model_dir",
          help="Specify the directory of pretrained model",
          default="",
      )
      parser.add_argument(
          "--data_dir",
          help="Specify the directory of data",
          default=config.DATA_DIR,
      )
      parser.add_argument(
          "--log_dir",
          help="Specify the directory of output",
          default=config.LOG_DIR,
      )
  
      parser.add_argument(
          "--dataset_name",
          help="Specify which dataset to use",
          choices=["h36m", "mpi"],
          default="h36m",
      )
      parser.add_argument(
          "--workers",
          help="Specify the number of workers for data loadering",
          default=config.NUM_WORKERS,
      )
      parser.add_argument(
          "--gpu",
          help="Specify the gpu to use for training",
          default="",
      )
      parser.add_argument(
          "--debug", action="store_true", help="Turn on the debug mode"
      )
      parser.add_argument(
          "--print_info",
          action="store_true",
          help="Whether to print detailed information in tqdm processing",
      )
      parser.add_argument(
          "--eval",
          action="store_true",
          help="Evaluate the model on the dataset(i.e. generate: joint_3d_pre)",
      )
      parser.add_argument(
          "--eval_suffix",
          default=None,
          help="Specify the suffix to save predictions on 3D in evaluation mode",
      )
      parser.add_argument(
          "--pretrain", default="", help="Whether to use pretrain model"
      )
      parser.add_argument(
          "--finetune_rotater",
          action="store_true",
          help="Load pretrained model and finetune rotater",
      )
      parser.add_argument("--print_interval", type=int, default=50)
      args = parser.parse_args()
      if args.cfg:
          update_config(args.cfg)
      else:
          print("Using default config...")
      update_dir(
          args.model_dir, args.log_dir, args.data_dir, args.debug
      )
      return args
  
imports: import _init_paths
  import os, sys, datetime, shutil
  import os.path
  import random
  import h5py
  import pickle
  import numpy
  import argparse
  import subprocess
  import warnings
  import torch
  import torch.nn
  import torch.optim
  import torch.backends.cudnn
  from torch.utils.data import TensorDataset, DataLoader
  from torch.optim import lr_scheduler
  from lib.dataloader.mpiinf import MPIINFDataset
  from lib.dataloader.h36m import Human36MDataset
  from lib.dataloader.surreal import SurrealDataset
  from lib.models.model import get_pose_model, get_discriminator
  from lib.core.config import config, update_config, update_dir
  from lib.utils.misc import save_pickle, create_logger, load_pickle
  from lib.utils.utils import load_checkpoint, save_checkpoint, get_optimizer
  from lib.utils.vis import plot_scalemid_dist, plot_scalemid_seq_dist
  from tensorboardX import SummaryWriter
label_desc: Use image node_id 2 for calling a global function with example usage: parse_args() and returns: args